

% This is "sig-alternate.tex" V1.9 April 2009
% This file should be compiled with V2.4 of "sig-alternate.cls" April 2009
%
% This example file demonstrates the use of the 'sig-alternate.cls'
% V2.4 LaTeX2e document class file. It is for those submitting
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% STRICTLY ADHERE TO THE SIGS (PUBS-BOARD-ENDORSED) STYLE.
% The 'sig-alternate.cls' file will produce a similar-looking,
% albeit, 'tighter' paper resulting in, invariably, fewer pages.
%
% ----------------------------------------------------------------------------------------------------------------
% This .tex file (and associated .cls V2.4) produces:
%       1) The Permission Statement
%       2) The Conference (location) Info information
%       3) The Copyright Line with ACM data
%       4) NO page numbers
%
% as against the acm_proc_article-sp.cls file which
% DOES NOT produce 1) thru' 3) above.
%
% Using 'sig-alternate.cls' you have control, however, from within
% the source .tex file, over both the CopyrightYear
% (defaulted to 200X) and the ACM Copyright Data
% (defaulted to X-XXXXX-XX-X/XX/XX).
% e.g.
% \CopyrightYear{2007} will cause 2007 to appear in the copyright line.
% \crdata{0-12345-67-8/90/12} will cause 0-12345-67-8/90/12 to appear in the copyright line.
%
% ---------------------------------------------------------------------------------------------------------------
% This .tex source is an example which *does* use
% the .bib file (from which the .bbl file % is produced).
% REMEMBER HOWEVER: After having produced the .bbl file,
% and prior to final submission, you *NEED* to 'insert'
% your .bbl file into your source .tex file so as to provide
% ONE 'self-contained' source file.
%
% ================= IF YOU HAVE QUESTIONS =======================
% Questions regarding the SIGS styles, SIGS policies and
% procedures, Conferences etc. should be sent to
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% For tracking purposes - this is V1.9 - April 2009

\documentclass{sig-alternate}
  \pdfpagewidth=8.5truein
  \pdfpageheight=11truein

\begin{document}
%
% --- Author Metadata here ---
\conferenceinfo{SAC'14}{March 24-28, 2014, Gyeongju, Korea.}
\CopyrightYear{2014} % Allows default copyright year (2002) to be over-ridden - IF NEED BE.
\crdata{978-1-4503-2469-4/14/03}  % Allows default copyright data (X-XXXXX-XX-X/XX/XX) to be over-ridden.
% --- End of Author Metadata ---

\title{Spatio-Temporal Correlation in Wireless Sensor Networks: A Strategy to
Maximize the Network Lifetime on Remote Data Query\titlenote{(Produces the
permission block, and copyright information). For use with SIG-ALTERNATE.CLS.
Supported by ACM.}}
% \subtitle{[Extended Abstract]
% \titlenote{A full version of this paper is available as
% \textit{Author's Guide to Preparing ACM SIG Proceedings Using
% \LaTeX$2_\epsilon$\ and BibTeX} at
% \texttt{www.acm.org/eaddress.htm}}}
%
% You need the command \numberofauthors to handle the 'placement
% and alignment' of the authors beneath the title.
%
% For aesthetic reasons, we recommend 'three authors at a time'
% i.e. three 'name/affiliation blocks' be placed beneath the title.
%
% NOTE: You are NOT restricted in how many 'rows' of
% "name/affiliations" may appear. We just ask that you restrict
% the number of 'columns' to three.
%
% Because of the available 'opening page real-estate'
% we ask you to refrain from putting more than six authors
% (two rows with three columns) beneath the article title.
% More than six makes the first-page appear very cluttered indeed.
%
% Use the \alignauthor commands to handle the names
% and affiliations for an 'aesthetic maximum' of six authors.
% Add names, affiliations, addresses for
% the seventh etc. author(s) as the argument for the
% \additionalauthors command.
% These 'additional authors' will be output/set for you
% without further effort on your part as the last section in
% the body of your article BEFORE References or any Appendices.

\numberofauthors{3} %  in this sample file, there are a *total*
% of EIGHT authors. SIX appear on the 'first-page' (for formatting
% reasons) and the remaining two appear in the \additionalauthors section.
%
\author{
% You can go ahead and credit any number of authors here,
% e.g. one 'row of three' or two rows (consisting of one row of three
% and a second row of one, two or three).
%
% The command \alignauthor (no curly braces needed) should
% precede each author name, affiliation/snail-mail address and
% e-mail address. Additionally, tag each line of
% affiliation/address with \affaddr, and tag the
% e-mail address with \email.
%
% 1st. author
\alignauthor
Angelo Brayner\\%\titlenote{.}\\
       \affaddr{University of Fortaleza}\\
       \affaddr{Fortaleza - Ceara - Brazil}\\
       \email{brayner@unifor.br}
% 2nd. author
\alignauthor Jose E. Bessa Maia\\%\titlenote{.}\\
       \affaddr{State University of Ceara}\\
       \affaddr{Fortaleza - Ceara - Brazil}\\
       \email{jose.maia@uece.br}
% 3rd. author
\alignauthor Fernando Rodrigues\titlenote{This author is the
one who did all the really hard work.}\\
       \affaddr{University of Fortaleza}\\
       \affaddr{Fortaleza - Ceara - Brazil}\\
       \email{fernandorodrigues@edu.unifor.br}
%\and  % use '\and' if you need 'another row' of author names
% 4th. author
% \alignauthor Lawrence P. Leipuner\\
%        \affaddr{Brookhaven Laboratories}\\
%        \affaddr{Brookhaven National Lab}\\
%        \affaddr{P.O. Box 5000}\\
%        \email{lleipuner@researchlabs.org}
% 5th. author
% \alignauthor Sean Fogarty\\
%        \affaddr{NASA Ames Research Center}\\
%        \affaddr{Moffett Field}\\
%        \affaddr{California 94035}\\
%        \email{fogartys@amesres.org}
% 6th. author
% \alignauthor Charles Palmer\\
%        \affaddr{Palmer Research Laboratories}\\
%        \affaddr{8600 Datapoint Drive}\\
%        \affaddr{San Antonio, Texas 78229}\\
%        \email{cpalmer@prl.com}
}
% There's nothing stopping you putting the seventh, eighth, etc.
% author on the opening page (as the 'third row') but we ask,
% for aesthetic reasons that you place these 'additional authors'
% in the \additional authors block, viz.

% \additionalauthors{Additional authors: John Smith (The
% Th{\o}rv{\"a}ld Group, email: {\texttt{jsmith@affiliation.org}})
% and Julius P.~Kumquat (The Kumquat Consortium, email:
% {\texttt{jpkumquat@consortium.net}}).}
\date{22 August 2013}

% Just remember to make sure that the TOTAL number of authors
% is the number that will appear on the first page PLUS the
% number that will appear in the \additionalauthors section.


\maketitle
\begin{abstract}
% This paper provides a sample of a \LaTeX\ document which conforms,
% somewhat loosely, to the formatting guidelines for
% ACM SIG Proceedings. It is an {\em alternate} style which produces
% a {\em tighter-looking} paper and was designed in response to
% concerns expressed, by authors, over page-budgets.
% It complements the document \textit{Author's (Alternate) Guide to
% Preparing ACM SIG Proceedings Using \LaTeX$2_\epsilon$\ and Bib\TeX}.
% This source file has been written with the intention of being
% compiled under \LaTeX$2_\epsilon$\ and BibTeX.
% 
% The developers have tried to include every imaginable sort
% of ``bells and whistles", such as a subtitle, footnotes on
% title, subtitle and authors, as well as in the text, and
% every optional component (e.g. Acknowledgments, Additional
% Authors, Appendices), not to mention examples of
% equations, theorems, tables and figures.
% 
% To make best use of this sample document, run it through \LaTeX\
% and BibTeX, and compare this source code with the printed
% output produced by the dvi file. A compiled PDF version
% is available on the web page to help you with the
% `look and feel'.
\end{abstract}

% A category with the (minimum) three required fields
\category{H.4}{Information Systems Applications}{Miscellaneous}
%A category including the fourth, optional field follows...
\category{D.2.8}{Software Engineering}{Metrics}[complexity measures, performance measures]

\terms{theory}

\keywords{Wireless sensor networks, Spatio-temporal correlation, Energy-efficient} 

\section{Introduction}
Wireless sensor networks (WSNs) has been widely used for capturing environmental
phenomena data in applications such as study and preservation of ecosystems,
forest fire alert-and-response and even to study certain species of trees
\cite{Tolle2005}. Although the technological advances in construction and
communication technologies between sensors, a key issue remains the energy
consumption of sensor nodes. Higher energy consumption (percentage of
consumption) is due to transmission and reception of data between sensors
(sensor data communication) \cite{Yick2008}. Higher accuracy comes at a higher
energy cost in many systems.

To the best of our knowledge, ecological and real sensor data (physical
environments) is usually strongly correlated \cite{Yoon2005}, so it is easier to
infer the state of a sensor (from its past and its proximity) \cite{Chu2006}.

Our method exploits \textit{Dissimilarity Measure} \cite{Liu2007} in order to
cluster the sensors by \textit{behavioral correlation}, which takes into account
not only the spatial proximity of the sensors, as the correlation between their
historical values.

WSNs can be categorized into three different types according to the application
or the usage pattern of the network. They can be used to i) answering queries,
ii) continuous sensing (stream) or iii) monitoring of sporadic events (alarms)
\cite{Ren2007}.
However, these uses are not mutually exclusive, that is, in a WSN that answering
queries, one can make queries requesting data to be sensed with a certain
frequency (continuous sensing) [or that alarms are triggered when certain events
occur] (or which alarms with triggered events). The case that stands out as
being the most critical use is the continuous sensing, as in this case, every
certain time (interval programmed), all sensor nodes need to take readings from
the environment and send the readings over the network from node to node until
they reach the data sink.

One way of reducing the energy consumption of sensor nodes and therefore
increase the (total) life time of a WSN is to make use of the spatial locality
principle from sensors, or that it takes into account proximity of the
location of the sensors in general also means an approximation of sensed values
(readings), especially when they concern environmental data \cite{Yick2008}.
Group sensors in clusters is the main technique used to take advantage of the
spatial locality principle for reducing energy consumption of the nodes, because
in this way, one can only use some representative nodes (e.g., one for cluster)
for reading / retrieval of the values of sensed data in a certain region
(cluster) whose sensors are spatially correlated \cite{Villas2012}. 

Another way to reduce the energy consumption of the sensor network, increasing
the total lifetime, is taking advantage of the temporal locality principle
/cite{Brayner2011}.
Therefore, in an outdoor environment without artificial interventions, most of
the time, sink can predict the value to be measured by sensors with a certain
error margin (threshold) using a historical readings (time series) and applying
a regression model \footnote{Several studies have proposed the use of this
technique, taking advantage of predictions across both time and space, with
different approaches.} (eg: linear, like $\bar{S}(t) = A + B(t)$).

The remainder of this document is concerned with showing, in
the context of an ``actual'' document, the \LaTeX\ commands
specifically available for denoting the structure of a
proceedings paper, rather than with giving rigorous descriptions
or explanations of such commands.

\section{Related Work}
In \cite{Vuran2004}, M representative nodes are chosen from set of N sensors
(where $M < N$) to send data to sink, based on criteria of calculating an
Distortion Function ($D(M)$) between the values sensed (readings) using a
threshold (\textit{distortion bound}) of maximum allowable distortion
($D_{max}$).
The spatial distance between the representative nodes directly influences the
Distortion Function using the correlation coefficient between the sensed values
by each node. $D(M)$ shows the relationship between the number of nodes
M contained in a region with N nodes ($M < N$) and the correlation coefficients
between nodes $n_i$ and $n_j$, and between the event source S and node $n_i$.
The work does not take into account the energy level of each node as choice criterion
of representative nodes and this factor is very important because the
restrictive characteristics in relation to energy charge of nodes in a WSN.

In EAST \cite{Villas2012}, sensors are grouped into two levels under an approach
to spatial correlation, while the leader and representative nodes perform a
temporal suppression technique. Leader node generates a representative value for
each cluster based on data received by representative nodes that form a subset
of all nodes sensing the same event. The sensed area (total) is divided into
"event areas" (major), which in turn are divided into "correlation regions (c)
or cells" (minor), where the former are managed by a "coordinator node" each and
the "correlation regions" are represented one by one by a "representative node"
for a single reading within this region is sufficient to represent it. The size
of the correlation region (c) may be incremented or decremented by the sink
according to the application and the characteristics of the event, to maintain
the accuracy of the data collected.

Another way to group sensor nodes into clusters is through dissimilarity
measures. In EEDC \cite{Liu2007}, such dissimilarity measures are calculated by
sink node for every pair of sensor nodes in the network, regardless of their
location. The dissimilarity measure between two nodes is calculated based on up
to three parameters, namely: differences in magnitude (\textit{M}) and trend
(\textit{T}) of data values and geographical / Euclidean distance between nodes
($g_{max}dist$).
The criterion for clustering is based on the maximum threshold of Dissimilarity
(max\_dst) defined by a tuple (\textit{M}, \textit{T}, $g_{max}dist$), based on
dissimilarity measure between nodes. Works as follows: 1) Initially, data sensed
by each node are sent in the form of a series for the sink. 2) The sink stores
all the data nodes sensors and then calculates the dissimilarity measure
(previously cited) to each pair of nodes in the network. 3) With the measures
calculated and the maximum threshold of dissimilarity (max\_dst), the sink
divides nodes into clusters.
The sink monitors large variations within a cluster and dynamically adjusts the
cluster in response to changes in spatial correlation. The sink node
recalculates dissimilarity of active sensor nodes (pairwise) within each group,
in the end of each time interval (time slot) after having collected the last
samples of each active sensor node. The current cluster should be divided if the
sink node to verify that there is at least one active sensor node reporting data
significantly different (ie, outside the threshold max\_dst with other nodes
in same cluster). This work also does not take into account the energy reserve
of each node as criteria for choosing representative nodes (an algorithm that
makes both the round robin scheduling, and selection of random representatives
nodes is used). Furthermore, the proposal does not address merge of nodes
(formation of new clusters from the union of other existing) that present
a low dissimilarity (ie, high similarity), because this could lead to greater
energy savings of network nodes.

The spatial correlation through clustering is addressed in \cite{Pham2010} as a
flooding algorithm where the sink node initiates passing messages to the other
nodes of the network, inviting them to form groups based on criteria such as a
measure of dissimilarity, in addition to physical proximity between nodes,
since, of course, message passing in a WSN is between adjacent nodes (ie,
geographically close).
Cluster Heads (\textit{CHs}) are selected mainly by two parameters:
i) sensor nodes that are one hop from the predecessor who sent the message to
calculate the dissimilarity measure with the average value reported in the
message, and then those who are within the threshold of dissimilarity applying
for \textit{CH}, where ii) \textit{CH} is proclaimed the one that has the highest level of energy
reserves. It should be observed that, during the process of clustering, are also
configured nodes that form the backbone of communication between each Cluster
Head and the sink node.
There is a round robin scheduling [inside](through) each cluster to decide which
member node is actived in each time slot making sensing and sending data to
[their](its) respective CH. Each cluster has a \textit{CH} and Gateway
(\textit{GW}), which connects one cluster with the neighbor cluster (or with the
sink node), in the 1st case, through an Extend Cluster (\textit{EC} or
\textit{EXT}). Additionally, each cluster may obviously have different members
(\textit{MEM}), and also several \textit{ECs}. The process of cluster split
(formation of new clusters) is delegate to \textit{GW} (Gateway) of each
cluster, and not to the \textit{CH} or the sink \footnote{There is no need to be
fired a \textit{CH} or sink for such a task}.
The weakness is due to the clustering process, where there is an intensive
exchange of messages for this, scattering a significant amount of energy charge
from sensor nodes, and the process of cluster reunification (merge) into new
unified clusters, that is only triggered by the sink using a global approach,
ie, for the entire network, not being provided partial merge of clusters.

In \cite{Shah2007}, the spatial correlation is explored through a mechanism
called GSC(Gridiron Spatial Correlation), where the sensed region has a Cluster
Head which will be the center of the region bounded by r (radius of the
monitored region), which is divided into regular correlated regions (quadratic)
according to the level of spatial density chosen, defined by $\theta$ (size of
the correlation region equal to $\theta^2$). Thus, the active sensors are
chosen according to two basic parameters: i) their proximity to the center of
the correlated regions and ii) their energy level must be above a certain
threshold, higher than their nearest neighbors. The scheduling of active nodes works
by passing a list to all the cluster head nodes (active and inactive, the latter
being only temporarily activated to receive the list) with the nodes to become
active each time slot where this setting only changes when any of the active
nodes gets your energy level below the threshold.
Although this solution considering the energy level of the nodes in the choice
for representative nodes (active), and the article mention that the sizes of the
rectangles can be reconfigured in addition to that they are considered
independent of each other, it does not explain how it is calculated the energy
threshold or how works this reconfiguration sizes rectangles and even gives
examples of the same.

In \cite{Chu2006}, the authors propose the use of distributed probabilistic
models by sensors nodes, who consider spatio temporal correlations between
readings of all network nodes, through the use of a probability distribution
function (pdf) and a transition model, in such a way that only would be sent to
the sink node the minimum set of data needed for that the values predicted
from such a model does not go beyond the maximum expected error. The positive
potential of this approach is probabilistic models represent various parameters
read in conjunction (attributes) different, for example, temperature, humidity,
light, etc.. On the other hand, nodes may need to communicate among themselves
to spatially correlate the data, which brings an energy cost of communication
intra-sensors. Furthermore, most of the overall computation done by the system
is executed by the sensor nodes (source), bringing even more energy expenditure
for the same. Thus, this approach becomes unsuitable for wireless sensor
networks.

\subsection{Proposed Approach}
Our approach, \textit{STCWSN}, uses two simple principles:
i) Cluster formation (or clustering) based on the \textit{behavioral
correlation} of the sensors (optionally using spatial correlation), obtained
from the time series of sensor readings with use of Dissimilarity Measures
\cite{Liu2007}, whence representative nodes are chosen, reduce sending data over
the network, and ii ) Using a linear regression model for the temporal
suppression of sending data (calculation of the regression equation
coefficients) through the maximum error level (threshold) desired by the user
used to control the data to be sent to the sink.



\subsection{Math Equations}
You may want to display math equations in three distinct styles:
inline, numbered or non-numbered display.  Each of
the three are discussed in the next sections.

\subsubsection{Inline (In-text) Equations}
A formula that appears in the running text is called an
inline or in-text formula.  It is produced by the
\textbf{math} environment, which can be
invoked with the usual \texttt{{\char'134}begin. . .{\char'134}end}
construction or with the short form \texttt{\$. . .\$}. You
can use any of the symbols and structures,
from $\alpha$ to $\omega$, available in
\LaTeX\cite{Lamport:LaTeX}; this section will simply show a
few examples of in-text equations in context. Notice how
this equation: \begin{math}\lim_{n\rightarrow \infty}x=0\end{math},
set here in in-line math style, looks slightly different when
set in display style.  (See next section).

\subsubsection{Display Equations}
A numbered display equation -- one set off by vertical space
from the text and centered horizontally -- is produced
by the \textbf{equation} environment. An unnumbered display
equation is produced by the \textbf{displaymath} environment.

Again, in either environment, you can use any of the symbols
and structures available in \LaTeX; this section will just
give a couple of examples of display equations in context.
First, consider the equation, shown as an inline equation above:
\begin{equation}\lim_{n\rightarrow \infty}x=0\end{equation}
Notice how it is formatted somewhat differently in
the \textbf{displaymath}
environment.  Now, we'll enter an unnumbered equation:
\begin{displaymath}\sum_{i=0}^{\infty} x + 1\end{displaymath}
and follow it with another numbered equation:
\begin{equation}\sum_{i=0}^{\infty}x_i=\int_{0}^{\pi+2} f\end{equation}
just to demonstrate \LaTeX's able handling of numbering.

\subsection{Citations}
Citations to articles \cite{bowman:reasoning,
clark:pct, braams:babel, herlihy:methodology},
conference proceedings \cite{clark:pct} or
books \cite{salas:calculus, Lamport:LaTeX} listed
in the Bibliography section of your
article will occur throughout the text of your article.
You should use BibTeX to automatically produce this bibliography;
you simply need to insert one of several citation commands with
a key of the item cited in the proper location in
the \texttt{.tex} file \cite{Lamport:LaTeX}.
The key is a short reference you invent to uniquely
identify each work; in this sample document, the key is
the first author's surname and a
word from the title.  This identifying key is included
with each item in the \texttt{.bib} file for your article.

The details of the construction of the \texttt{.bib} file
are beyond the scope of this sample document, but more
information can be found in the \textit{Author's Guide},
and exhaustive details in the \textit{\LaTeX\ User's
Guide}\cite{Lamport:LaTeX}.

This article shows only the plainest form
of the citation command, using \texttt{{\char'134}cite}.
This is what is stipulated in the SIGS style specifications.
No other citation format is endorsed or supported.

\subsection{Tables}
Because tables cannot be split across pages, the best
placement for them is typically the top of the page
nearest their initial cite.  To
ensure this proper ``floating'' placement of tables, use the
environment \textbf{table} to enclose the table's contents and
the table caption.  The contents of the table itself must go
in the \textbf{tabular} environment, to
be aligned properly in rows and columns, with the desired
horizontal and vertical rules.  Again, detailed instructions
on \textbf{tabular} material
is found in the \textit{\LaTeX\ User's Guide}.

Immediately following this sentence is the point at which
Table 1 is included in the input file; compare the
placement of the table here with the table in the printed
dvi output of this document.

\begin{table}
\centering
\caption{Frequency of Special Characters}
\begin{tabular}{|c|c|l|} \hline
Non-English or Math&Frequency&Comments\\ \hline
\O & 1 in 1,000& For Swedish names\\ \hline
$\pi$ & 1 in 5& Common in math\\ \hline
\$ & 4 in 5 & Used in business\\ \hline
$\Psi^2_1$ & 1 in 40,000& Unexplained usage\\
\hline\end{tabular}
\end{table}

To set a wider table, which takes up the whole width of
the page's live area, use the environment
\textbf{table*} to enclose the table's contents and
the table caption.  As with a single-column table, this wide
table will ``float" to a location deemed more desirable.
Immediately following this sentence is the point at which
Table 2 is included in the input file; again, it is
instructive to compare the placement of the
table here with the table in the printed dvi
output of this document.


\begin{table*}
\centering
\caption{Some Typical Commands}
\begin{tabular}{|c|c|l|} \hline
Command&A Number&Comments\\ \hline
\texttt{{\char'134}alignauthor} & 100& Author alignment\\ \hline
\texttt{{\char'134}numberofauthors}& 200& Author enumeration\\ \hline
\texttt{{\char'134}table}& 300 & For tables\\ \hline
\texttt{{\char'134}table*}& 400& For wider tables\\ \hline\end{tabular}
\end{table*}
% end the environment with {table*}, NOTE not {table}!

\subsection{Figures}
Like tables, figures cannot be split across pages; the
best placement for them
is typically the top or the bottom of the page nearest
their initial cite.  To ensure this proper ``floating'' placement
of figures, use the environment
\textbf{figure} to enclose the figure and its caption.

This sample document contains examples of \textbf{.eps}
and \textbf{.ps} files to be displayable with \LaTeX.  More
details on each of these is found in the \textit{Author's Guide}.

% \begin{figure}
% \centering
% \epsfig{file=fly.eps}
% \caption{A sample black and white graphic (.eps format).}
% \end{figure}

% \begin{figure}
% \centering
% \epsfig{file=fly.eps, height=1in, width=1in}
% \caption{A sample black and white graphic (.eps format)
% that has been resized with the \texttt{epsfig} command.}
% \end{figure}


As was the case with tables, you may want a figure
that spans two columns.  To do this, and still to
ensure proper ``floating'' placement of tables, use the environment
\textbf{figure*} to enclose the figure and its caption.
% \begin{figure*}
% \centering
% \epsfig{file=flies.eps}
% \caption{A sample black and white graphic (.eps format)
% that needs to span two columns of text.}
% \end{figure*}
and don't forget to end the environment with
{figure*}, not {figure}!

Note that either {\textbf{.ps}} or {\textbf{.eps}} formats are
used; use
the \texttt{{\char'134}epsfig} or \texttt{{\char'134}psfig}
commands as appropriate for the different file types.

% \begin{figure}
% \centering
% \psfig{file=rosette.ps, height=1in, width=1in,}
% \caption{A sample black and white graphic (.ps format) that has
% been resized with the \texttt{psfig} command.}
% \vskip -6pt
% \end{figure}

\subsection{Theorem-like Constructs}
Other common constructs that may occur in your article are
the forms for logical constructs like theorems, axioms,
corollaries and proofs.  There are
two forms, one produced by the
command \texttt{{\char'134}newtheorem} and the
other by the command \texttt{{\char'134}newdef}; perhaps
the clearest and easiest way to distinguish them is
to compare the two in the output of this sample document:

This uses the \textbf{theorem} environment, created by
the\linebreak\texttt{{\char'134}newtheorem} command:
\newtheorem{theorem}{Theorem}
\begin{theorem}
Let $f$ be continuous on $[a,b]$.  If $G$ is
an antiderivative for $f$ on $[a,b]$, then
\begin{displaymath}\int^b_af(t)dt = G(b) - G(a).\end{displaymath}
\end{theorem}

The other uses the \textbf{definition} environment, created
by the \texttt{{\char'134}newdef} command:
\newdef{definition}{Definition}
\begin{definition}
If $z$ is irrational, then by $e^z$ we mean the
unique number which has
logarithm $z$: \begin{displaymath}{\log e^z = z}\end{displaymath}
\end{definition}

Two lists of constructs that use one of these
forms is given in the
\textit{Author's  Guidelines}.

There is one other similar construct environment, which is
already set up
for you; i.e. you must \textit{not} use
a \texttt{{\char'134}newdef} command to
create it: the \textbf{proof} environment.  Here
is a example of its use:
\begin{proof}
Suppose on the contrary there exists a real number $L$ such that
\begin{displaymath}
\lim_{x\rightarrow\infty} \frac{f(x)}{g(x)} = L.
\end{displaymath}
Then
\begin{displaymath}
l=\lim_{x\rightarrow c} f(x)
= \lim_{x\rightarrow c}
\left[ g{x} \cdot \frac{f(x)}{g(x)} \right ]
= \lim_{x\rightarrow c} g(x) \cdot \lim_{x\rightarrow c}
\frac{f(x)}{g(x)} = 0\cdot L = 0,
\end{displaymath}
which contradicts our assumption that $l\neq 0$.
\end{proof}

Complete rules about using these environments and using the
two different creation commands are in the
\textit{Author's Guide}; please consult it for more
detailed instructions.  If you need to use another construct,
not listed therein, which you want to have the same
formatting as the Theorem
or the Definition\cite{salas:calculus} shown above,
use the \texttt{{\char'134}newtheorem} or the
\texttt{{\char'134}newdef} command,
respectively, to create it.

\subsection*{A {\secit Caveat} for the \TeX\ Expert}
Because you have just been given permission to
use the \texttt{{\char'134}newdef} command to create a
new form, you might think you can
use \TeX's \texttt{{\char'134}def} to create a
new command: \textit{Please refrain from doing this!}
Remember that your \LaTeX\ source code is primarily intended
to create camera-ready copy, but may be converted
to other forms -- e.g. HTML. If you inadvertently omit
some or all of the \texttt{{\char'134}def}s recompilation will
be, to say the least, problematic.

\section{Conclusions}
This paragraph will end the body of this sample document.
Remember that you might still have Acknowledgments or
Appendices; brief samples of these
follow.  There is still the Bibliography to deal with; and
we will make a disclaimer about that here: with the exception
of the reference to the \LaTeX\ book, the citations in
this paper are to articles which have nothing to
do with the present subject and are used as
examples only.
%\end{document}  % This is where a 'short' article might terminate

%ACKNOWLEDGMENTS are optional
\section{Acknowledgments}
This section is optional; it is a location for you
to acknowledge grants, funding, editing assistance and
what have you.  In the present case, for example, the
authors would like to thank Gerald Murray of ACM for
his help in codifying this \textit{Author's Guide}
and the \textbf{.cls} and \textbf{.tex} files that it describes.

%
% The following two commands are all you need in the
% initial runs of your .tex file to
% produce the bibliography for the citations in your paper.
\bibliographystyle{abbrv}
\bibliography{sigproc}  % sigproc.bib is the name of the Bibliography in this case
% You must have a proper ".bib" file
%  and remember to run:
% latex bibtex latex latex
% to resolve all references
%
% ACM needs 'a single self-contained file'!
%
%APPENDICES are optional
%\balancecolumns
\appendix
%Appendix A
\section{Headings in Appendices}
The rules about hierarchical headings discussed above for
the body of the article are different in the appendices.
In the \textbf{appendix} environment, the command
\textbf{section} is used to
indicate the start of each Appendix, with alphabetic order
designation (i.e. the first is A, the second B, etc.) and
a title (if you include one).  So, if you need
hierarchical structure
\textit{within} an Appendix, start with \textbf{subsection} as the
highest level. Here is an outline of the body of this
document in Appendix-appropriate form:
\subsection{Introduction}
\subsection{The Body of the Paper}
\subsubsection{Type Changes and  Special Characters}
\subsubsection{Math Equations}
\paragraph{Inline (In-text) Equations}
\paragraph{Display Equations}
\subsubsection{Citations}
\subsubsection{Tables}
\subsubsection{Figures}
\subsubsection{Theorem-like Constructs}
\subsubsection*{A Caveat for the \TeX\ Expert}
\subsection{Conclusions}
\subsection{Acknowledgments}
\subsection{Additional Authors}
This section is inserted by \LaTeX; you do not insert it.
You just add the names and information in the
\texttt{{\char'134}additionalauthors} command at the start
of the document.
\subsection{References}
Generated by bibtex from your ~.bib file.  Run latex,
then bibtex, then latex twice (to resolve references)
to create the ~.bbl file.  Insert that ~.bbl file into
the .tex source file and comment out
the command \texttt{{\char'134}thebibliography}.
% This next section command marks the start of
% Appendix B, and does not continue the present hierarchy
\section{More Help for the Hardy}
The sig-alternate.cls file itself is chock-full of succinct
and helpful comments.  If you consider yourself a moderately
experienced to expert user of \LaTeX, you may find reading
it useful but please remember not to change it.
%\balancecolumns % GM June 2007
% That's all folks!
\end{document}
